Referring　expression　comprehension　aims　to　locate　a　target　object　in　an　image　described　by　a　referring　expression,　where　extracting　semantic　and　discriminative　visual　information　plays　an　important　role.　Most　existing　methods　either　ignore　attribute　information　or　context　information　in　the　model　learning　procedure,　thus　resulting　in　less　effective　visual　features.　In　this　paper,　we　propose　a　Multi-level　Attention　Network　(MANet)　to　extract　the　target　attribute　information　and　the　surrounding　context　information　simultaneously　for　the　target　object,　where　the　Attribute　Attention　Module　is　designed　to　extract　the　fine-grained　visual　information　related　to　the　referring　expression　and　the　Context　Attention　Module　is　designed　to　merge　the　context　information　of　surroundings　to　learn　more　discriminative　visual　features.　Experiments　on　various　common　benchmark　datasets　show　the　effectiveness　of　our　approach.&　COPY;　2023　Elsevier　B.V.　All　rights　reserved.